Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System

The particle filter (PF) provides a kind of novel technique for estimating the hidden states of the nonlinear and/or non-Gaussian systems. However, the general PF always suffers from the particle impoverishment problem, which can lead to the misleading state estimation results. To cope with this pro...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on industrial electronics (1982) Jg. 62; H. 6; S. 3852 - 3861
Hauptverfasser: Yin, Shen, zhu, xiangping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Dynamical systems
Equations
Fault detection
genetic algorithm
Genetic algorithms
Genetics
hidden state estimation
Industrial electronics
intelligent particle filter
Mathematical model
Non-Gaussian
Nonlinear systems
Nonlinearity
Particle filter
Real time
real-time fault detection
State estimation
Strategy
Genetic algorithm (GA)
particle filter (PF)
intelligent particle filter (IPF)
hidden state estimation
real-time fault detection
ISSN:0278-0046, 1557-9948
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The particle filter (PF) provides a kind of novel technique for estimating the hidden states of the nonlinear and/or non-Gaussian systems. However, the general PF always suffers from the particle impoverishment problem, which can lead to the misleading state estimation results. To cope with this problem, a modified particle filter, i.e., intelligent particle filter (IPF), is proposed in this paper. It is inspired from the genetic algorithm. The particle impoverishment in general PF mainly results from the poverty of particle diversity. In IPF, the genetic-operators-based strategy is designed to further improve the particle diversity. It should be pointed out that the general PF is a special case of the proposed IPF with the specified parameters. Two experiment examples show that IPF mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF. Finally, the proposed IPF is implemented for real-time fault detection on a three-tank system, and the results are satisfactory.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2015.2399396